What is research data management (RDM)?

This new article series presents the basics of research data management (RDM). The opening section of the series provides an overview of RDM: what it is, why it concerns all researchers, and how the RDM life cycle relates to the research life cycle.

(Tämä artikkeli on saatavilla myös suomeksi.)

Text: Mikko Ojanen, Tanja Lindholm & Liisa Siipilehto

Over time, RDM has been identified as a set of tasks that are separate from the scientific processes of a research project. RDM is the mechanical, managerial, and technical handling of research data, whereas the core content of a research project involves methodological or analytical data processing.

The tasks related to RDM are not entirely new; however, previously they have not been viewed as significant separate processes. Researchers typically have a good command of research life cycle tasks and their management is well covered in higher education. The processes related to an RDM life cycle have remained more or less hidden, yet the RDM tasks are the oil that keeps the wheels of the research life cycle running (see Figure 1).

Figure 1. Research Life Cycle and Data Management Life Cycle as separate but synchronous processes. CC-BY UH Data Support.

RDM is about taking proper care of data and it involves all aspects of the data management life cycle: from planning data management to archiving data. It is of particular significance that data management planning (DMP) is an integral part of ever evolving RDM. Therefore, the changes in the RDM processes have an effect on a project’s data management plan, which should be updated frequently.

RDM skills are fundamental research skills and are required by everyone handling a project’s research data. Alongside the growing acknowledgement of RDM as a separate process, the need for new expertise – and even a new profession – has gradually emerged. Learning RDM skills and integrating them into a project workflow is often a slow process – at first. However, once the skills become routine and the tools are adopted, RDM becomes tacit knowledge – a researcher’s every day know-how – that can be applied across multiple projects.

Learning RDM skills and integrating them into a project workflow is often a slow process – at first. However, once the skills become routine and the tools are adopted, RDM becomes tacit knowledge – a researcher’s every day know-how – that can be applied across multiple projects.

A researcher can save significant resources (both time and money) by paying attention to the RDM components and learning how to avoid RDM hiccups. This will also ensure that the project is conducted effectively. Mastering the RDM life cycle and its components will help keep the research life cycle running smoothly. A research project can be severely hindered or even halted when the components in the RDM life cycle are interrupted.

We will take a closer look at the different components of RDM in the next part of this article series.

  Research Data Management – know your data!

Research data management (RDM) is a crucial part of any research. First and foremost, the aim of RDM is to make the research process as efficient as possible. Second, RDM will help you meet the expectations and requirements of your organisation and research funders. RDM skills are fundamental skills for researchers and they apply to everyone who handles data for research projects. By learning RDM, you get to KNOW YOUR DATA!

In this series, the University of Helsinki Data Support team introduces the key components of RDM and data management planning (DMP): what they are, why they are important, and where to look for more help with RDMP (research data management planning) issues. The series comprises six parts:

1) What is research data management (RDM)? (3.9.2020)
2) The components of research data management (17.9.2020)
3) Why research data management? (30.9.2020)
4) Why plan research data management in advance? (22.10.2020)
5) Effective research data management? – DMP to the rescue! (19.11.2020)
6) Where is the help and support for research data management? (8.12.2020)

 

3 vastausta artikkeliin “What is research data management (RDM)?”

Kommentit on suljettu.